{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bbd1b7a1-dbb7-4243-99e0-70a6cd47d573",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bcc2f5482d8342a7915cecf9e7855531",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "306958c8-4603-4b9b-b941-6a824777164d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import librosa\n",
    "import math\n",
    "import pyarrow as pa\n",
    "import pandas as pd\n",
    "from datasets import load_dataset_builder, SplitGenerator, Split, Dataset, table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4ac69d3b-38c6-49af-aefe-63755bf3f0e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "SAMPLE_RATE = 16_000\n",
    "MAX_LENGTH_IN_SECONDS = 20.0\n",
    "\n",
    "def add_audio(file, words):\n",
    "    audio, _ = librosa.load(file, sr=SAMPLE_RATE)\n",
    "    return {\n",
    "        \"audio\": audio,\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9192b631-388f-4306-b975-9ba770b9dc4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "audio, _ = librosa.load('clips/1.wav', sr=SAMPLE_RATE)\n",
    "    \n",
    "df = pd.DataFrame({\n",
    "    'audio': [audio],\n",
    "    'text': ['bjorn.'],\n",
    "})\n",
    "tbl = table.InMemoryTable(\n",
    "    pa.Table.from_pandas(df)\n",
    ")\n",
    "ds = Dataset(tbl, split=[\"test\", \"training\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f37d68ea-cbe7-4dd1-8215-f9449fe047f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.save_to_disk(\"data/test/\")\n",
    "ds.save_to_disk(\"data/training/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bac1a601-a7a1-434e-917d-0e372684f56b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Resuming upload of the dataset shards.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "61cfa14ccb514ff4961072752bc3d4da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5803c7d37ce1426794af8ad65f618275",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading metadata:   0%|          | 0.00/1.20k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Updating downloaded metadata with the new split.\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Split ['test', 'training'] already present",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [12]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpush_to_hub\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msharpcoder/bjorn_training\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py:4342\u001b[0m, in \u001b[0;36mDataset.push_to_hub\u001b[0;34m(self, repo_id, split, private, token, branch, max_shard_size, shard_size, embed_external_files)\u001b[0m\n\u001b[1;32m   4340\u001b[0m         repo_info\u001b[38;5;241m.\u001b[39mdataset_size \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m dataset_nbytes\n\u001b[1;32m   4341\u001b[0m         repo_info\u001b[38;5;241m.\u001b[39msize_in_bytes \u001b[38;5;241m=\u001b[39m repo_info\u001b[38;5;241m.\u001b[39mdownload_size \u001b[38;5;241m+\u001b[39m repo_info\u001b[38;5;241m.\u001b[39mdataset_size\n\u001b[0;32m-> 4342\u001b[0m         repo_info\u001b[38;5;241m.\u001b[39msplits[split] \u001b[38;5;241m=\u001b[39m SplitInfo(\n\u001b[1;32m   4343\u001b[0m             split, num_bytes\u001b[38;5;241m=\u001b[39mdataset_nbytes, num_examples\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m), dataset_name\u001b[38;5;241m=\u001b[39mdataset_name\n\u001b[1;32m   4344\u001b[0m         )\n\u001b[1;32m   4345\u001b[0m         info_to_dump \u001b[38;5;241m=\u001b[39m repo_info\n\u001b[1;32m   4346\u001b[0m buffer \u001b[38;5;241m=\u001b[39m BytesIO()\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/splits.py:523\u001b[0m, in \u001b[0;36mSplitDict.__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m    521\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot add elem. (key mismatch: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m != \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    522\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 523\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSplit \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m already present\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    524\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__setitem__\u001b[39m(key, value)\n",
      "\u001b[0;31mValueError\u001b[0m: Split ['test', 'training'] already present"
     ]
    }
   ],
   "source": [
    "# ds.push_to_hub(\"sharpcoder/bjorn_training\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b070517c-2dfc-4f1b-baed-1748a9d5f088",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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